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MotionSC: Data Set and Network for Real-Time Semantic Mapping in Dynamic Environments

2022; Institute of Electrical and Electronics Engineers; Volume: 7; Issue: 3 Linguagem: Inglês

10.1109/lra.2022.3188435

2377-3766

Joey Wilson, Jingyu Song, Yuewei Fu, Arthur Zhang, Andrew Capodieci, Paramsothy Jayakumar, Kira Barton, Maani Ghaffari,

Human Pose and Action Recognition

This work addresses a gap in semantic scene completion (SSC) data by creating a novel outdoor data set with accurate and complete dynamic scenes. Our data set is formed from randomly sampled views of the world at each time step, which supervises generalizability to complete scenes without occlusions or traces. We create SSC baselines from state-of-the-art open source networks and construct a benchmark real-time dense local semantic mapping algorithm, MotionSC, by leveraging recent 3D deep learning architectures to enhance SSC with temporal information. Our network shows that the proposed data set can quantify and supervise accurate scene completion in the presence of dynamic objects, which can lead to the development of improved dynamic mapping algorithms.